





'''simple docstring'''


import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
a__			:					Optional[int]       						=						logging.get_logger(__name__)


a__			:					int       						=						['model.decoder.embed_positions.weights']
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any] )							->     Tuple:

       """simple docstring"""


       if "emb" in name:
              UpperCAmelCase  =						name.replace('''emb'''			,	'''model.decoder.embed_tokens''' )
       if "transformer" in name:
              UpperCAmelCase  =						name.replace('''transformer'''			,	'''model.decoder''' )
       if "cross_attention" in name:
              UpperCAmelCase  =						name.replace('''cross_attention'''			,	'''encoder_attn''' )
       if "linear1" in name:
              UpperCAmelCase  =						name.replace('''linear1'''			,	'''fc1''' )
       if "linear2" in name:
              UpperCAmelCase  =						name.replace('''linear2'''			,	'''fc2''' )
       if "norm1" in name:
              UpperCAmelCase  =						name.replace('''norm1'''			,	'''self_attn_layer_norm''' )
       if "norm_cross" in name:
              UpperCAmelCase  =						name.replace('''norm_cross'''			,	'''encoder_attn_layer_norm''' )
       if "norm2" in name:
              UpperCAmelCase  =						name.replace('''norm2'''			,	'''final_layer_norm''' )
       if "out_norm" in name:
              UpperCAmelCase  =						name.replace('''out_norm'''			,	'''model.decoder.layer_norm''' )
       if "linears" in name:
              UpperCAmelCase  =						name.replace('''linears'''			,	'''lm_heads''' )
       if "condition_provider.conditioners.description.output_proj" in name:
              UpperCAmelCase  =						name.replace('''condition_provider.conditioners.description.output_proj'''			,	'''enc_to_dec_proj''' )
       return name
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			OrderedDict			,	SCREAMING_SNAKE_CASE_ :			int )							->     Tuple[Dict, Dict]:

       """simple docstring"""


       UpperCAmelCase  =						list(state_dict.keys() )
       UpperCAmelCase  =						{}
       for key in keys:
              UpperCAmelCase  =						state_dict.pop(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						rename_keys(SCREAMING_SNAKE_CASE_ )
              if "in_proj_weight" in key:
                     # split fused qkv proj
                     UpperCAmelCase  =						val[:hidden_size, :]
                     UpperCAmelCase  =						val[hidden_size : 2 * hidden_size, :]
                     UpperCAmelCase  =						val[-hidden_size:, :]
              elif "enc_to_dec_proj" in key:
                     UpperCAmelCase  =						val
              else:
                     UpperCAmelCase  =						val
       return state_dict, enc_dec_proj_state_dict
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str )							->     MusicgenDecoderConfig:

       """simple docstring"""


       if checkpoint == "small":
              # default config values
              UpperCAmelCase  =						1_024
              UpperCAmelCase  =						24
              UpperCAmelCase  =						16
       elif checkpoint == "medium":
              UpperCAmelCase  =						1_536
              UpperCAmelCase  =						48
              UpperCAmelCase  =						24
       elif checkpoint == "large":
              UpperCAmelCase  =						2_048
              UpperCAmelCase  =						48
              UpperCAmelCase  =						32
       else:
              raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}." )
       UpperCAmelCase  =						MusicgenDecoderConfig(
           hidden_size=SCREAMING_SNAKE_CASE_			,	ffn_dim=hidden_size * 4			,	num_hidden_layers=SCREAMING_SNAKE_CASE_			,	num_attention_heads=SCREAMING_SNAKE_CASE_			,	)
       return config
@torch.no_grad()
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			int=None			,	SCREAMING_SNAKE_CASE_ :			List[str]=None			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]="cpu" )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						MusicGen.get_pretrained(SCREAMING_SNAKE_CASE_			,	device=SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE_ )

       UpperCAmelCase  =						fairseq_model.lm.state_dict()
       UpperCAmelCase,					UpperCAmelCase  =						rename_state_dict(
           SCREAMING_SNAKE_CASE_			,	hidden_size=decoder_config.hidden_size )

       UpperCAmelCase  =						TaEncoderModel.from_pretrained('''t5-base''' )
       UpperCAmelCase  =						EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
       UpperCAmelCase  =						MusicgenForCausalLM(SCREAMING_SNAKE_CASE_ ).eval()

       # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
       UpperCAmelCase,					UpperCAmelCase  =						decoder.load_state_dict(SCREAMING_SNAKE_CASE_			,	strict=SCREAMING_SNAKE_CASE_ )

       for key in missing_keys.copy():
              if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
                     missing_keys.remove(SCREAMING_SNAKE_CASE_ )

       if len(SCREAMING_SNAKE_CASE_ ) > 0:
              raise ValueError(f"Missing key(s) in state_dict: {missing_keys}" )

       if len(SCREAMING_SNAKE_CASE_ ) > 0:
              raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}" )

       # init the composite model
       UpperCAmelCase  =						MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE_			,	audio_encoder=SCREAMING_SNAKE_CASE_			,	decoder=SCREAMING_SNAKE_CASE_ )

       # load the pre-trained enc-dec projection (from the decoder state dict)
       model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE_ )

       # check we can do a forward pass
       UpperCAmelCase  =						torch.arange(0			,	8			,	dtype=torch.long ).reshape(2			,	-1 )
       UpperCAmelCase  =						input_ids.reshape(2 * 4			,	-1 )

       with torch.no_grad():
              UpperCAmelCase  =						model(input_ids=SCREAMING_SNAKE_CASE_			,	decoder_input_ids=SCREAMING_SNAKE_CASE_ ).logits

       if logits.shape != (8, 1, 2_048):
              raise ValueError('''Incorrect shape for logits''' )

       # now construct the processor
       UpperCAmelCase  =						AutoTokenizer.from_pretrained('''t5-base''' )
       UpperCAmelCase  =						AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''			,	padding_side='''left''' )

       UpperCAmelCase  =						MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE_			,	tokenizer=SCREAMING_SNAKE_CASE_ )

       # set the appropriate bos/pad token ids
       UpperCAmelCase  =						2_048
       UpperCAmelCase  =						2_048

       # set other default generation config params
       UpperCAmelCase  =						int(30 * audio_encoder.config.frame_rate )
       UpperCAmelCase  =						True
       UpperCAmelCase  =						3.0

       if pytorch_dump_folder is not None:
              Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
              logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}" )
              model.save_pretrained(SCREAMING_SNAKE_CASE_ )
              processor.save_pretrained(SCREAMING_SNAKE_CASE_ )

       if repo_id:
              logger.info(f"Pushing model {checkpoint} to {repo_id}" )
              model.push_to_hub(SCREAMING_SNAKE_CASE_ )
              processor.push_to_hub(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    a__			:					Tuple       						=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--checkpoint',
        default='small',
        type=str,
        help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
    )
    parser.add_argument(
        '--pytorch_dump_folder',
        required=True,
        default=None,
        type=str,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument(
        '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
    )
    parser.add_argument(
        '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
    )

    a__			:					List[str]       						=						parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b == 0:
								return 1
				if (b % 2) == 0:
								return actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)
				else:
								return a * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b < 0:
								return 1 / actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				print(power(-2, -3))




